SEAD: Sensor Event-Based Anomaly Detection for Smart Home Automation

Abstract

As smart IoT devices become increasingly common in our homes, there is a growing demand for seamless automation and synchronization between these devices. A key challenge in achieving this automation is accurately identifying and grouping related sensors, which are essential for generating automated operational policies to control the actuators. However, this process is often hindered by anomalous data in sensor readings, which can obscure the sensor relationships identified during the grouping process. These anomalies disrupt the accuracy of the sensor groupings and, as a result, compromise the effectiveness and reliability of the automation policies generated for managing smart environments. In this paper, we introduce SEAD, a novel approach for detecting anomalies by first calculating the total number of sensor events within a specified time window, followed by the use of an unsupervised learning method for anomaly detection. We evaluate the effectiveness of this method by leveraging existing sensor inference techniques and testing it on three custom datasets and one public dataset. Our experimental results show that applying SEAD to remove anomalies improves the quality of sensor groupings for smart home automation.

Department(s)

Computer Science

Document Type

Conference Proceeding

DOI

10.1109/COMPSAC65507.2025.00187

Keywords

Clustering Scores, Sensor Grouping, Time Frequency, Time Series Data, Time Window

Publication Date

1-1-2025

Journal Title

Proceedings 2025 IEEE 49th Annual Computers Software and Applications Conference Compsac 2025

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